Previous studies focused on the status and driving factors of non-grain cultivated land (NGCL), but lacked research on the impact mechanism of NGCL on sustainable agricultural development from the perspective of farmers’ household livelihoods and agricultural production factor allocation. Therefore, the concept of resilience was introduced. According to official statistics of China from 2010 to 2021, such as the local statistical yearbooks, the impact of NGCL on agricultural development resilience (ADR) in the main grain-producing area of Northeast China was explored based on the threshold effect model and the spatial lag model. The results indicate that: (1) the overall level of NGCI in the study area from 2011 to 2020 show an upward followed by a downward trend; (2) the size of agricultural labor force and average area per labor constrain the impact of NGCI on ADR, and the change can be characterized by negative to positive, increasing and then decreasing respectively, and the former is more constrained than the latter; (3) a negative effect of the NGCI trend on ADR exists without spatial spillover effect. The expansion of food production exacerbates the risk of factor mismatch, which is accentuated by the governance environment that pursues food production excessively. Establishing the NFP governance standards should consider the transformation of farmers’ livelihoods and the optimization of production factor allocation. Constructing a resilient risk management mechanism, promoting moderate scale operation and optimizing agricultural labor scale are specific paths for improving the governance mechanisms of NGCI. This study provides a theoretical reference for the development of policies and governance strategies for NGCI in underdeveloped areas.

Exploring the spatiotemporal dynamics, spatial mismatch, and complex influencing mechanism of grain production and cropland productivity in the black soil region of northeast China (BSRNC) is essential for the synergistic protection and utilization of black soil cropland and sustainable grain production. The BSRNC has realized cropland expansion and grain production increases in the past decades. This implied a substantial investment has been made in the region’s agriculture. However, at present, knowledge on the spatial mismatch and influencing factors of grain production and cropland productivity is still unclear. This study analyzed the spatial–temporal mismatch characteristics of grain production and cropland net primary productivity (CNPP) using the gravity center model, spatial autocorrelation analysis, and spatial mismatch index (SMI), and identified the spatial heterogeneity and prediction–response relationships of influencing factors based on a geographically and temporally weighted regression (GTWR) model and boosted regression tree (BRT) machine learning algorithm. The findings indicated that grain production and CNPP have been increasing, but the overall spatial pattern of cold hotspots has not changed obviously in the BSRNC from 2000 to 2020. The SMI has shown a decreasing trend, indicating that the synergistic development of grain production and CNPP has been obvious, which plays an important role in sustainable food supply capacity. Agricultural production and the natural environment have always been critical factors influencing the spatial mismatch. Specifically, the marginal impact of fertilizer application has undergone a shift. This study may provide new clues for the formulation of regional strategies for sustainable food supply and black soil cropland system protection.

In recent years, ecological concerns such as vegetation destruction, permafrost deterioration, and river drying have been paid much more attention to on the Yellow River Basin in China. Soil pH is regarded to be the fundamental variable among soil properties for vegetation growth, while net primary productivity (NPP) is also an essential indicator to reflect the healthy growth of vegetation. Due to the limitation of on-site samples, the spatial–temporal variations in soil pH and NPP, as well as their intrinsic mechanisms, remain unknown, especially in the Yellow River source area, China. Therefore, it is imperative to investigate the coupling relationship between soil pH and NPP of the area. The study coupled MODIS reflectance data (MOD09A1) with on-site soil pH to estimate spatial–temporal variations in soil pH, explore the response of NPP to soil pH, and assess the extent to which they contribute to grassland ecosystems, thus helping to fill knowledge gaps. Results indicated that the surface spectral reflectance for seven bands could express the geographic pattern of soil pH by applying a multiple linear regression equation; NPP exhibited an increasing trend while soil pH was the contrary in summer from 2000 to 2021. In summer, NPP was negatively correlated with soil pH and there was a lag effect in the response of NPP to soil pH, revealing a correlation between temperate steppes > montane meadows > alpine meadows > swamps in different grassland ecosystems. In addition, contribution indices for temperate steppes and montane meadows were positive whereas they were negative for swamps and alpine meadows, which are apparent findings. The contribution index of montane and alpine meadows was greater than that of temperate steppes and swamps. The approach of the study can enable managers to easily identify and rehabilitate alkaline soil and provides an important reference and practical value for ecological restoration and sustainable development of grassland ecosystems in alpine regions.